import os
import sys
import tarfile
 
import cv2
import numpy as np
import tensorflow as tf
 
 
sys.path.append("..")
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

#immport tensorflow as tf
from tensorflow.python.platform import gfile
 
# Path to frozen detection graph
CWD_PATH = os.getcwd()
 
PATH_TO_CKPT = os.path.join(CWD_PATH,'frozen_inference_graph.pb')
 
# List of the strings that is used to add correct label for each box.
 
PATH_TO_LABELS = os.path.join(CWD_PATH,'majiang.pbtxt')
 
 
 
NUM_CLASSES = 28
 
detection_graph = tf.Graph()

#with gfile.FastGFile(PATH_TO_CKPT, 'rb') as f:
#    graph_def = tf.GraphDef()
#    graph_def.ParseFromString(f.read())
#    tf.import_graph_def(graph_def, name='')
#    tf.train.write_graph(graph_def, './', 'majiang.pbtxt', as_text=True)


#with tf.gfile.FastGFile(PATH_TO_CKPT, "rb") as f:
# 
#    graph_def = tf.GraphDef()
#     
#    graph_def.ParseFromString(f.read())
#     
#    tf.import_graph_def(graph_def, name='')
#     
#    tf.io.write_graph(graph_def, "./", 'frozen_model_test.txt',as_text=True)


with detection_graph.as_default():
 
    od_graph_def = tf.GraphDef()
 
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
 
        serialized_graph = fid.read()
 
        od_graph_def.ParseFromString(serialized_graph)

        tf.import_graph_def(od_graph_def, name='')
 
 
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
 
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
 
category_index = label_map_util.create_category_index(categories)
 
 

         


def load_image_into_numpy_array(image):
 
    (im_width, im_height) = image.size
 
    return np.array(image.getdata()).reshape(
 
      (im_height, im_width, 3)).astype(np.uint8)
 
 
 
with detection_graph.as_default():
 
    with tf.Session(graph=detection_graph) as sess:
 
        image_np = cv2.imread("./1_2.jpg")
 
        cv2.imshow("input", image_np)
 
        print(image_np.shape)
 
        # image_np == [1, None, None, 3]
 
        image_np_expanded = np.expand_dims(image_np, axis=0)
 
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
 
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
 
        scores = detection_graph.get_tensor_by_name('detection_scores:0')
 
        classes = detection_graph.get_tensor_by_name('detection_classes:0')
 
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
 
        # Actual detection.
 
        (boxes, scores, classes, num_detections) = sess.run(
 
            [boxes, scores, classes, num_detections],
 
            feed_dict={image_tensor: image_np_expanded})
 
        # Visualization of the results of a detection.
 
        #print(num_detections)
        vis_util.visualize_boxes_and_labels_on_image_array(
              image_np,

              np.squeeze(boxes),
 
              np.squeeze(classes).astype(np.int32),
 
              np.squeeze(scores),
 
              category_index,
 
              use_normalized_coordinates=True,
 
              min_score_thresh=0.4,
 
              line_thickness=3)
 
        cv2.imshow('object detection', image_np)
 
        cv2.imwrite("run_result.png", image_np)
 
        cv2.waitKey(0)
 
        cv2.destroyAllWindows()
 
 
sess.close()